A new hybrid stochastic-robust optimization approach for self-scheduling of generation companies

This paper presents a new mixed-integer linear programming model for day-ahead self-scheduling of generating companies integrating the underlying ideas of robust optimization (RO) and stochastic programming (SP) to cope with the uncertainties of electricity market prices and availability/unavailability of units. The proposed hybrid approach models the uncertainty of electricity market prices by bounded intervals instead of probability distributions, aiming to derive a more tractable optimization model. Conservatism against uncertain electricity market prices is adjusted by a certain parameter named budget of robustness. Also, a renovated Markov chain approach considering the chance of return (i.e., return rate) for failed units, in addition to forced outage rate of units, in each hour of the scheduling period is introduced in this paper to produce a set of scenarios modeling the availability/unavailability of units. Therefore, the proposed hybrid self-scheduling approach benefits from both the tractability of RO and modeling accuracy of SP. The proposed approach is implemented on IEEE 118-bus test system under different circumstances to illustrate its effectiveness compared to deterministic self-scheduling model as well as the models only employing RO or SP. Copyright (c) 2015 John Wiley & Sons, Ltd.